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Machine Learning and Deep Learning Optimization Algorithms for Unconstrained Convex Optimization Problem
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DOI：10.1109/ACCESS.2024.3522361.png)
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Abstract
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This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) and Nesterov Accelerated Gradient (NAG) with modern techniques such as Adaptive Moment Estimation (Adam), Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP). Through empirical experiments, convergence speed, solution accuracy and robustness, is evaluated providing insights to aid algorithm selection. The convergence dynamics of convex optimization, is explored analyzing classical algorithms and contemporary neural network (NN) methodologies. The study concludes with a comparative assessment of these algorithms performance metrics and their respective strengths and weaknesses.
Keywords:
Optimization
Convex functions
Convergence
Training
Long short term memory
Heuristic algorithms
Machine learning algorithms
Linear programming
Accuracy
Adaptation models
Adaptive moment estimation (Adam)
convex optimization
deep learning (DL)
gradient descent (GD)
long short term memory (LSTM) networks
machine learning (ML)
mathematical optimization
multilayer perceptron (MLP)
nesterov accelerated gradient (NAG)
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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